scholarly journals Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 2966-2974
Author(s):  
Yuan Li ◽  
Zhuojian Wang ◽  
Zhe Li ◽  
Zihan Jiang
Keyword(s):  
Support Vector Machine ◽  
Phase Space ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Pressure Signal ◽  
Signal Prediction

scholarly journals Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

2020 ◽  
Vol 38 (4) ◽  
pp. 933-940
Author(s):  
Yan Wang ◽  
Zhongshui Man ◽  
Meihua Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Energy Efficient ◽  
Coalbed Methane ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Proposed Model ◽  
Bayesian Evidence

The productivity of coalbed methane (CBM) depends heavily on the heat environment, and directly reflects the quality of the well. Following the theories of phase space reconstruction and Bayesian evidence framework, this paper puts forward a Bayes-least squares-support vector machine (Bayes-LS-SVM) model for the prediction of energy-efficient productivity of CBM under Bayesian evidence network based on chaotic time series. The energy-efficient productivity stands for the gas and water production of CBM wells at a low energy consumption, despite the disturbance from the heat environment. The proposed model avoids the local optimum trap of backpropagation neural network (BPNN), and overcomes the main defects of the SVM: high time consumption of parameter determination, and proneness to overfitting. In our model, the model parameters are optimized through three-layer Bayesian evidence inference, and the input vector for prediction is selected adaptively. In this way, the model construction is not too empirical, and the constructed model is highly adaptive. Then, the theory on phase space reconstruction was applied to investigate the chaotic property of the time series on CBM production, and the Bayes-LS-SVM was adopted to predict the time series after phase space reconstruction, in comparison with neural network prediction methods like SVM and BPNN. Experimental results show that the proposed model boast quick computing, accurate fitting, flexible structure, and strong generalization ability.

Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults

2019 ◽  
Vol 51 (2) ◽  
pp. 102-113 ◽  
Author(s):  
Simranjit Kaur ◽  
Sukhwinder Singh ◽  
Priti Arun ◽  
Damanjeet Kaur ◽  
Manoj Bajaj
Keyword(s):  
Feature Selection ◽  
Phase Space ◽  
Performance Test ◽  
Phase Space Reconstruction ◽  
Behavioral Disorder ◽  
Support Vector ◽  
Reconstruction Technique ◽  
Eyes Closed ◽  
Eyes Open ◽  
And Control

Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.

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